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A geometric view on inner transformation between the variables of a linear regression model
ID Li, Zhaoyang (Author), ID Antončič, Boštjan (Author)

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Abstract
In the teaching and researching of linear regression analysis, it is interesting and enlightening to explore how the dependent variable vector can be inner-transformed into regression coefficient estimator vector from a visible geometrical view. As an example, the roadmap of such inner transformation is presented based on a simple multiple linear regression model in this work. By applying the matrix algorithms like singular value decomposition (SVD) and Moore-Penrose generalized matrix inverse, the dependent variable vector lands into the right space of the independent variable matrix and is metamorphosed into regression coefficient estimator vector through the three-step of inner transformation. This work explores the geometrical relationship between the dependent variable vector and regression coefficient estimator vector as well as presents a new approach for vector rotating.

Language:English
Keywords:econometrics, regression analysis, matrix singular value decomposition, Moore-Penrose generalized inverse, matrix inner transformation
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication status:Published
Publication version:Version of Record
Year:2021
Number of pages:Str. 931-938
Numbering:Vol. 12, no. 10
PID:20.500.12556/RUL-134026 This link opens in a new window
UDC:51-7
ISSN on article:2152-7385
DOI:10.4236/am.2021.1210061 This link opens in a new window
COBISS.SI-ID:83841027 This link opens in a new window
Publication date in RUL:22.12.2021
Views:864
Downloads:150
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Record is a part of a journal

Title:Applied mathematics
Shortened title:Appl. math.
Publisher:Scientific Research Publishing, Inc.
ISSN:2152-7385
COBISS.SI-ID:1462364 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:29.10.2021

Secondary language

Language:Slovenian
Keywords:ekonometrija, regresijske analize

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